{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- import\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- figure size\n",
    "plt.rcParams['figure.figsize'] =[8, 6]\n",
    "\n",
    "# ---------- axes\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "\n",
    "# ---------- ticks\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8.0\n",
    "plt.rcParams['ytick.major.size'] = 8.0\n",
    "\n",
    "# ---------- lines\n",
    "plt.rcParams['lines.linewidth'] = 2.5\n",
    "\n",
    "# ---------- grid\n",
    "plt.rcParams['grid.linestyle'] = ':'\n",
    "\n",
    "# ---------- font\n",
    "plt.rcParams['font.size'] = 20\n",
    "#plt.rcParams['pdf.fonttype'] = 42    # embed fonts in PDF using type42 (True type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pkl(filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Select</th>\n",
       "      <th>Spg_num</th>\n",
       "      <th>Spg_sym</th>\n",
       "      <th>Spg_num_opt</th>\n",
       "      <th>Spg_sym_opt</th>\n",
       "      <th>E_eV_atom</th>\n",
       "      <th>Magmom</th>\n",
       "      <th>Opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>P4_32_12</td>\n",
       "      <td>96</td>\n",
       "      <td>P4_32_12</td>\n",
       "      <td>-3.395741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>P4_32_12</td>\n",
       "      <td>96</td>\n",
       "      <td>P4_32_12</td>\n",
       "      <td>-3.299576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>P2_1/m</td>\n",
       "      <td>11</td>\n",
       "      <td>P2_1/m</td>\n",
       "      <td>-3.117206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>P31c</td>\n",
       "      <td>159</td>\n",
       "      <td>P31c</td>\n",
       "      <td>-3.022127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>Pba2</td>\n",
       "      <td>32</td>\n",
       "      <td>Pba2</td>\n",
       "      <td>-2.973559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>190</td>\n",
       "      <td>P-62c</td>\n",
       "      <td>190</td>\n",
       "      <td>P-62c</td>\n",
       "      <td>-2.958895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>P4mm</td>\n",
       "      <td>99</td>\n",
       "      <td>P4mm</td>\n",
       "      <td>-2.947536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>F222</td>\n",
       "      <td>22</td>\n",
       "      <td>F222</td>\n",
       "      <td>-2.927870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Pmna</td>\n",
       "      <td>53</td>\n",
       "      <td>Pmna</td>\n",
       "      <td>-2.650477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "      <td>I-43m</td>\n",
       "      <td>217</td>\n",
       "      <td>I-43m</td>\n",
       "      <td>-2.551110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Select  Spg_num   Spg_sym  Spg_num_opt Spg_sym_opt  E_eV_atom  Magmom   Opt\n",
       "6       3       96  P4_32_12           96    P4_32_12  -3.395741     NaN  done\n",
       "4       2       96  P4_32_12           96    P4_32_12  -3.299576     NaN  done\n",
       "9       4       11    P2_1/m           11      P2_1/m  -3.117206     NaN  done\n",
       "7       2      159      P31c          159        P31c  -3.022127     NaN  done\n",
       "0       5       32      Pba2           32        Pba2  -2.973559     NaN  done\n",
       "2       5      190     P-62c          190       P-62c  -2.958895     NaN  done\n",
       "5       1       99      P4mm           99        P4mm  -2.947536     NaN  done\n",
       "1       4       22      F222           22        F222  -2.927870     NaN  done\n",
       "8       3       53      Pmna           53        Pmna  -2.650477     NaN  done\n",
       "3       1      217     I-43m          217       I-43m  -2.551110     NaN  done"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rslt_data = load_pkl('./pkl_data/rslt_data.pkl')\n",
    "\n",
    "# ---------- sort Selection\n",
    "#rslt_data.head(10)\n",
    "\n",
    "# ---------- sort by Energy\n",
    "rslt_data.sort_values(by=['E_eV_atom']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of selection: 5\n",
      "Number of data: 10\n",
      "Success: 10\n",
      "Error: 0\n",
      "Emin: -3.3957405 eV/atom\n"
     ]
    }
   ],
   "source": [
    "# ---------- Selection\n",
    "smax = rslt_data['Select'].max()\n",
    "print('Number of selection: {}'.format(smax))\n",
    "\n",
    "# ---------- Number of structures\n",
    "ndata = len(rslt_data)\n",
    "print('Number of data: {}'.format(ndata))\n",
    "\n",
    "# ---------- check success and error\n",
    "nsuccess = rslt_data['E_eV_atom'].count()\n",
    "nerror = ndata - nsuccess\n",
    "print('Success: {}'.format(nsuccess))\n",
    "print('Error: {}'.format(nerror))\n",
    "\n",
    "# ---------- minimum\n",
    "Emin = rslt_data['E_eV_atom'].min()\n",
    "print('Emin: {} eV/atom'.format(Emin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy vs. trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV/atom)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAGeCAYAAAAnjiPFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydd3wc5Zn4v49kNcuycQEb7BibYhwMZ4JzoSYICIQkl3KpFy4FUu7SLuWS+11CCpBLLuXSySWXCgmE5HLpldCMIRAgscEIjLARFsKyZSFbtnrd5/fHzNrr9a52V+9Is+/o+X4++xlp5p3ZZ74zWj37zltEVTEMwzAMw4iKirgDMAzDMAwjWVhyYRiGYRhGpFhyYRiGYRhGpFhyYRiGYRhGpFhyYRiGYRhGpFhyYRiGYRhGpFhyYRiGYRhGpFhyYRiGYRhGpFhyYSQaEblDRFRELos7liQQl0+frqNPsaYRkQYR+aKItIjISBh/a9xxzWR8vI8yseTCc0TkuvAGzH6Ni8heEfmTiPyriNTFHatRvojIZSJylYicFncs5UyCPf0ceD9wHDAI7AaejjWiDLI+5zYWKHtDWO66aQpvQkRktoi8Q0R+IyJtIjIgIv0isl1Efioir0/i5/OsuAMwImMU2Jvxey0wHzgnfL1FRBpVtWw+MKaJNuAxYH/cgZQ5lwHnAa3AgxOUi8tnuVzHyyjsqVxiLQoRWQM8n+Az5Hmqem/MIRXidBF5har+PO5ACiEiLwG+BSzJWN0PpIAV4euVwGdF5A2qevt0xzhVWHKRHO5R1cbMFSIyD3gr8DngZOAzwFumP7T4UNU3xh1DkojLp0/X0adYQ9aEy4c8SCzSfEJEfqmqqbgDyUf4OOO7BE8IHgM+CfxBVfeE2+cRJHXvBhqB5wGJSS7ssUiCUdX9qvoFghsc4CVxxmMYRlmSrpLvizWK4tgADBAkRJfGHEteRGQt8D8E/2N/DzxLVW9IJxZw4PP5Z6p6PvAPQG880U4NllzMDB4Kl/XZG0RkkYi8U0R+JSLNItIbPg/cEjbwOibHPitEJBU+1zwl35uKyBwR6QvLXZxj+yki8r3w2eOQiOwTkbtF5O0iUpXnmNUi8l4RuScsPyoiu0Vks4j8t4iclVU+b6OoyZx7xr6t4XEbRWRBWH67iAyLSLuIfFtEjs63fyFE5BUicpOIPB0ec4eI/FBETi8ypuUi8h0ReSp0u11EPh9+W8rc5zIRUYKqfoBrs9rutGaVz+kz672PFpH/Cd97UEQeFZH3i0hFRvlXi8hd4TXsEZHfFbiXDnvfjHWFXldlHWsy93zRnia65zLKlHR9p+J+k6DtiALXhavOyzqnxojjXioiXxeRJ8J9J3r8lo8O4Gvhz1eJSEm175O59pPkk0AN0A5cqqqDExVW1f8FvhjRe5cHqmovj18EHwwK3DFBmW+GZTbn2Pb5cJsSPHPdA4xlrOsE/ibHfjeH278wwfu+JSzzJFCRte3dwHjG+/Rmve96YHbWPrOAOzLKpIDurP1+nLVPuvxlUZ17uG9rWOb1GT/3A0MZ+28H5pd4PSuA72ccYyw8x/Tv48A7CsT01jD2tNfBjP23AUdn7PNagg/skXD7/vD39OsvxfjMeO/LgV0Zx8r0eU1Y9jMZ59aTsb0bODHPuR32vgSNEDsmeKWPe5XrdS/FU4F7blLXdyruN+CDYez7w/1Hss7p7Ajj/ieCBqLpuPuAByfxOfdjYGFGzG/LUfaGcNt1Uf7NlxDrUoLPJgX+n8Nx8t5HPrxiD8BejhdwguQCmAu8j4P/xC/LUeY9wIeBU4FZ4bpKYB1wU7jfw4Bk7feacNvu9H45jv2nsMwnsta/PFzfA/wbsChcXw28ANgabv9m1n5vzPhwej1QmxHvcuBdwIez9sn7BzrZcw/LpT80u4EHgLPC9bOAl3Lwg/dzJV7PD3Ewcfoo0BCuXwr8hIMf5M+bIKZ9BEnEueH6CuBlHPxwvznHvkV9kOUrl/Xe9xB+QAOzw/NIn9MVBP/E3gvUh2VOAZrDMj8p5X0niPPtHPyH+dwIr3vBOArcc5O6vlN1v4XHuIzCX1Bc4+4lqEHNTFhOKCHG68j48gBcFf7eBtRklZ0ouZj0tS8h1n/kYLKy2uE4Jd3z5faKPQB7OV7Ag3902d869mXc4JuAN0zi2DXAI+ExzsvaVs3Bf1Yvy7HvqowPo5UZ6yszPnBekOd9jydIIEY59Fv218P9vlHCOUzqD3Sicw+3p8+hA1iYY/sHwu1PlPCeczj4jezTObZXAneF2++cIKbBXB/cwPkZ98S5k/GUr1zGe+8Fjsix320Z7/3xHNufG24bAqpdrmN4rHQNQ85aHofrXjCOCRxN+vpOxf2Wse9lTJBcRBR3N7C41NgyjnMdhyYXcwlqHRR4b1bZvMmFy7Uv4TifzLiXXZKUou/5cnxZm4vkUAUsznhlPltfABwlIlLKAVV1GLgl/PWcrG0jwA/CX9+cY/fLw+Udqro9Y30jcCzwsKr+Mc/7tgD3Enwra8zY1BMuJ92WoVgmOvcsvqUZjbQy+GW4XCkih7V1ycNFBB+aIwQ9fLJjGgf+I/z1uSKyJLtMyE9U9fEc+68nqFUAeFWRMZXK/6jqvhzrbw2XI+R+tnw3wYdxDXDCZN9cRJYDPyP4e/imqn6jlP1LuO6TIYrrG+X9VixRxP0DVd0dVUCq2pMRy4ejOOcIr/3CcNmtYZYwE7HkIjlsUFVJvwj+MR8HvJPgm8fnge/k2lFEVovI10TkobBxXbqxphJUXwPkauiUPt6LRGRxxvEqCR5hwMGeKmnODpcnikhHvldGuWdk7PuHcPkyEfl12LhsIQ44nHuav+RZ357x8xFFhpNuFLdZVbvzlLmToPo5s3w2d0zwHhsK7OtKU571neGyVVUP65WgQZfCrvDX+ZN5YxGZTfBP9kiCb9L/MkFZ1+s+GaK4vlHeb8USRdx/jjgmgGsIHssuJnjcURSTvfYicqKI/EiChsoDIrJVRD4tIlH7Lkg5xZIPG+cioYTfJrYD3xCRJwieJ75ZRK5V1T+ly4nIPxDUQKR7Z6QIqkCHw9/nEPQyOeybgao+KiL3ECQCrwe+EG66hOCPcz9Bo7tM0rUONQQfCoWYnfF+G0Tk48DHCbrVviQ8h2bgdwTfVLcVcUzC/SZ97hnk7D6mqkMZFUU5e77k4Mhw2Z6vQHjcLgJ3R+Yplnf/jG359nVlV5714wW2Z5Yp1lc21wLPImhA/EpVHc1VKKLrPhmiuL5R3m/FEkXckQ/ep6oDIvKfwFeAfxORr6vqhAOXTfbai8gzgPsJ/H+D4HyeTdBmrBHI7KWWrlmaLyISde1FibHEhtVczADCxw8d4a+vSa8XkSOBbxP8of0vwQ1aq6rzVXWJqi4BvpQunufw3w6Xl2esS//8Iz28C1b6nvtVZk3LBK+rss7lPwjac3wY+CPBo5LVBM+ct4hIUQMYRXTuU0XtNL+f94jIFQT3dj9BG6Cc/8zK5Lr7en1d4h4vXGRSfBN4iqC26wMTFXS89m8gqBF6sar+p6p+W1X/GfgqcKaIPDOj7KPhsgY4afKnlpdSYokNSy5mDm3h8riMdS8kyNS3EPTF3pjj216h2oWfEPyDXyMifysiizg4WNf3cpRPP3ddXnTkWajqdlX9jKpeQtCe5HyCatlZwNdF5KgiDhPFuUdN+h9iXjciUsvBZ7r5vg1OVJ2f3paYYeBF5O8InvmnG79tnqB4nNc9qus73ZRt3GE7iXR7j/eFnz/5cLn26TZs2TVv6d8HMtZtILgXIejJEzWlxBIbllzMHJaGy8w/pmXh8iHNMYxu2AD0gokOqqoDwI/CX99M0A2rmqDBZq7nw+lnr38jIktzbC8JVR1X1TuAvyM4t3qCbySFcD73KWBTuDxxAjfP4+DjzE15ypw3wXukt2Xvm3Yw3bU0ToTf0n5I8Fn2SVX9aYFdXK+7i6eoru90U+5xXwu0AA0EXWbz4XLt022VrhWR00VkmYj8PcGjiB+q6pPpgqq6g2BUToB/EZG5xZxECQ3ui44lTiy5mAGIyDkcTC4y//DTzydPyXNjv42gW2gh0o9G/iHcBw5vyJnmNoJqzErgvyY6qIjMz/q9eoLiIxyseq2Z6LghUZ17lNxMUAtURfBBcQhhQ9mPhb/epaod2WVCXisix2WvFJHncbAV/P9lbU73xCmbBmGFCBuv/YqgJ8MvgSuL2M31urt4iur6TjdlHbeqjhGMewFBA/Z8vckmfe1V9fcE99eFwEaCz7CfE/wd5XoU+1GCdhzLgBvDmp28iMhrgH+dqIxDLLFgyUWCEZE6EXk5B2sWBjj0UcWtBNV3pwBfTbc0FpG5IvJvwH9zsHFSXlR1I8EMkUcQjPk/QtDXPFfZUYLRORV4nYj8UjKmrxaRKhF5toh8jqBBaiY/EJFrReQFItKQsc8KgtEDawnGeLirUMxRnXuUqGo/8J/hr+8RkY+IyJwwrqUE1/FcDg5klI8R4A8icna4b4UEszOmv9Xfoqp3Z+3zSLh8hWQNEV7G/Bg4kWDQozcU2XDO9bpP2lOE13da8STuGwked9SRv/bB9do/QdCV+53A3xM0JP1nghFnD0FVHyQY1E+BFwMPSDC1+oJ0GRGZJ0GPt/UEbUAaso8TRSyxUeyAGPYqzxf5B9FKD3CVfvUBf5dj/y9mlevm4IieN3FwQJjrCsTxroxj/LSIuC8nyOzT+wxw+FC8mrXPLzO2pYf+7s9YN0bWYGFMPFripM+dg4MDNU5wjunjrijhelZy+DDLezk4nPA48M48+6Zjyh7+eyDjeIcM/52x7+qM6zFK0DOgFfhTMT4L+aC4USDzHiPX+2acUw8TDwP+wQive0FPBe65SV3fqbrfSrg2UxZ3kTFeR8YgWnnKvDLruua6fpO69gS1skNkDAgYrv9E6ODUPDG9nKCdWeZ79nLosPcaesoe3TTnfTTZWKb7ZTUXySF7EK1FBAnFQwRdRNeo6m+zd1LVfyUY9/8Bgg/NyvDn9xFk3GNFvn9ml9NcDTmz3/dagpbUXyb4NjjOwVH37iCo9stuaf0h4P8RfAg8QdC2o5Lgeeu1wOmqen2R8UZ57pGhQRuSNxEMcnUzwUircwgaa/0IeI6qfr3AYR4naHfyPYKq4PSoqF8Anq2qh3UHVdVmgsGSbgr3WUIw2Nmy7LJlSAOH3vvZrzmZhV2uu6uniK7vtONJ3D+nQHsPh2v/ToJxPrJrU39J0P4m56BbqvpLgkb07yJoh7GDoG3KLIK/yZ8SzO56kqreOeHZOcYy3UiY8RiGEyLyjwSPQtqBYzUYZ8OYRiSYmfNY4HwNGrkahhEBIvIY0Keq67LWPwe4D/gXVf1azp0THMtEJKbmQkQ+KyK3ycFpnveKyAMicqWUOIpj2Pr2eyKyU4KpgVtF5MvZDQyNQ3h7uPyeJRaGYSSMxwh6uK3JWv/6cLlxhsaSl8TUXIjICEGV2BaC5831wJkE1cM7gTNV9akijnM8QUOZowhaojcDzyEYS+Ex4BzNPbb/jEVE3kIwFPgwcJyq7ow5pBmJ1VwYxtQgIs8Fbid4FPY1grY8FwCvJphl+AUzMZaJSNLw33NVdSh7pYh8imCa5w8TPKsqxNcJEov3qOo1Gcf5IvB+4FMc/JY+YxGRZQRTqjcQDGQFwXTPllgYhpEoVPUuETmToMvrWwn+R7QDnwWunqmxTERiai7yISJrCbpJ3qqqFxUoezxBY7hW4HjNGGgl7Pq4i6DBzFEadM+asYTdP7cTtE5+kmCsi89qjsFpjOnBai4MwygXklRzkY/0UNQPFVH2/HB5c/Y/SVXtFZG7gYsJHrfcFl2I/qGqrXg2mmPSUdUVccdgGIYBCUwuROSDBF2k5hG0tziXILEoZnCRdNfHrXm2byNILlaRkVyIyAMEMwE+PrmoDcMwDMM7TgCeVtVnZW9IXHIBfJBDJ5+5iWAQkmIm00mPuJdv2t70+uyhf4+srq5eOjIykneujGOPPZZFixYxZ84cent7aWhooK+vjzlz5jAwMEBtbS3Dw8NUVVUxPj6OiCAijI+PU1VVxfDwMHV1dfT39x9yjPSyv7+furo6hoeHqa6uZmxsjIqKoDNQKpVi1qxZjIyMUFNTw+DgIPX19Ycdo6+vj/r6egYHB6mpqWF0dJTKysoDg6JUVlYyOjpKTU0NQ0NDzJ49+8A52DnZOdk52TnZOc2sc2pqaqK3tzfn/7zEJRcaTJuLiCwGziaosXhARP5OVadqQp3H165du/T++++fosPPDLZs2cLJJ58cdxjeYx7dMYfumEN3yt1hY2MjGzZsyFljn5hxLrJR1d2q+guCxxgLgR8UsVu6ZiLfnAHp9fuyN9TWTjgvjVEEK1eujDuERGAe3TGH7phDd3x2mNjkIo0G089uAdaIyKICxR8Ll6vybD8xXB7WJmN4eHhyARoH2LnTerFGgXl0xxy6Yw7d8dlh4pOLkGPCZaGRI9eHy4tF5BA3YVfUcwgmgbo3e8eqqirXGGc8CxYsKFzIKIh5dMccumMO3fHZYSKSCxFZlWv643Cq6U8RDDJyj6p2h+urRGR1OK7FAVS1hWBSnhUEE81kcjXBqJ/X5xrjYnzcRrx2ZWBgIO4QEoF5dMccumMO3fHZYVIadL4I+LSI/IlgYKc9BD1GziOYka4DeFtG+aXAowSDP63IOtY7CYb//qqIXBiWO4NgDIytwEdyBSBiQz64km4RbbhhHt0xh+6YQ3d8dpiU5OJWgv625wLPIugq2k+QDFwPfFVV9xZzIFVtEZFnA58ALiFIXHYBXwGuTtd+ZGPJhTv2aCkazKM75tAdc+iOzw4TkVyo6sPAu0so38oEo0uGE5xdXkoM9ljEnb6+PhYtKtTm1iiEeXTHHLpjDt3x2aG/dS5lhs8ZZrng6x9RuWEe3TGH7phDd3x2aMlFRFhXVHd27NgRdwiJwDy6Yw7dMYfu+OzQkouIqKurizsE7znhhBPiDiERmEd3zKE75tAdnx1achER/f0zegb2SHjkkUfiDiERmEd3zKE75tAdnx1achERc+bMiTsE71m7dm3cISQC8+iOOXTHHLrjs0NLLiIi38xwRvFs3Lgx7hASgXl0xxy6Yw7d8dmhJRcR0dDQEHcI3rNu3bq4Q0gE5tEdc+iOOXTHZ4eWXESE1Vy443OWXk6YR3fMoTvm0B2fHVpyERFWc+GOz1l6OWEe3TGH7phDd3x2aMlFRFhvEXeampriDiERmEd3zKE75tAdnx1achERNs6FO6tWrYo7hERgHt0xh+6YQ3d8dmjJRUTYCJ3utLW1xR1CIjCP7phDd8yhOz47tOQiIqqrq+MOwXsWL14cdwiJwDy6Yw7dMYfu+OzQkouIGBsbizsE79m3b1/cISQC8+iOOXTHHLrjs0NLLiKiosJUulJbWxt3CInAPLpjDt0xh+747ND+IxqGYRiGESmWXEREKpWKOwTvGRoaijuERGAe3TGH7phDd3x2aMlFRMyaNSvuELzniCOOiDuERGAe3TGH7phDd3x2aMlFRIyMjMQdgvfs3r077hASgXl0xxy6Yw7d8dmhJRcRUVNTE3cI3rN8+fK4Q0gE5tEdc+iOOXTHZ4eWXETE4OBg3CF4z9atW+MOIRGYR3fMoTvm0B2fHVpyERH19fVxh+A9p556atwhJALz6I45dMccuuOzQ0suIsKmXHfH5+mFywnz6I45dMccuuOzQ0suIsKmXHfH5+mFywnz6I45dMccuuOzQ0suIsJqLtzxOUsvJ8yjO+bQHXPojs8OLbmICKu5cMfnLL2cMI/umEN3zKE7Pju05CIi+vr64g7BezZv3hx3CInAPLpjDt0xh+747NCSi4iw3iLurFmzJu4QEoF5dMccumMO3fHZoSUXEWHjXLjz+OOPxx1CIjCP7phDd8yhOz47tOQiImyETneWLVsWdwiJwDy6Yw7dMYfu+OzQkouIGB0djTsE7+nq6oo7hERgHt0xh+6YQ3d8dpiI5EJEForIW0XkFyLyuIgMish+EfmTiLxFRIo+TxFpFRHN8+rIt19lZWU0JzODmTNnTtwhJALz6I45dMccuuOzw6TME/5q4BvALmA90AYsBl4BfAd4oYi8WlW1yOPtB76cY33eLiHFH9rIh9X+RIN5dMccumMO3fHZYVKSi63AS4HfqWoqvVJErgDuB15JkGj8rMjj7VPVq0oJwJILd1KpVOFCRkHMozvm0B1z6I7PDhPxWERVb1fV32QmFuH6DuB/wl8bpzIGeyzizuzZs+MOIRGYR3fMoTvm0B2fHSYiuShAul5prIR9akTk9SJyhYi8V0TOF5EJswefq6/Khb1798YdQiIwj+6YQ3fMoTs+O0zKY5GciMgs4I3hrzeVsOsS4PqsddtF5HJV3ZBrB+uK6s4xxxwTdwiJwDy6Yw7dMYfu+Oww6TUXnwFOAX6vqn8scp9rgQsJEox64FTgm8AK4A8isjbXTkNDQ87BznS2b98edwiJwDy6Yw7dMYfu+OwwscmFiLwH+ADQDLyh2P1U9eqwDcduVR1Q1YdV9e3AF4E64Kpc++3cuRMRyfu68sor2bRpE3BwprtNmzaRSqXYsmULg4ODtLS00N3dTXt7O7t27aKrq4vW1lb6+vpobm5mbGzswFjz6WOkl01NTQwPD7Nt2zZ6enpoa2ujs7OTzs5O2tra6OnpYdu2bQwPD9PU1JTzGJs3b2ZsbIzm5mb6+vpobW2lq6uLXbt20d7eTnd3Ny0tLQwODrJlyxZSqVSk5zQwMJC4c4rjOi1evDhx5zTd12lsbCxx5zTd10lVE3dO032djjnmmLI+p4m+VEsSezmIyLuBa4AtwIVhw07XY54AbAP2qurCrG13rFu37ry//vWvrm8zo9m0aROnn3563GF4j3l0xxy6Yw7dKXeHjY2NbNiwYYOqNmZvS1zNhYi8jyCxeBg4P4rEIuTpcJlzhjKfBzspF8r5j8gnzKM75tAdc+iOzw4TlVyIyL8DXwIeJEgsOiM8/Jnh8olcG3t7eyN8q5lJujrOcMM8umMO3TGH7vjsMDHJhYh8jKAB50aCRyF5B2UXkSoRWS0ix2etf6aIHFYzISIrgK+Fv96Q65gNDQ2TjNxIs27durhDSATm0R1z6I45dMdnh4lILkTkTcAngHHgLuA9InJV1uuyjF2WAo8Ct2Ud6rVAh4j8TkS+LiKfFZGfhmVPAH4PfD5XDH19eUcGN4ok3aDIcMM8umMO3TGH7vjsMCnjXKwMl5XA+/KU2QBcV+A464GTgGcB5xC0r9gH/Ilg3Ivr881PYm0u3DnttNPiDiERmEd3zKE75tAdnx0mouZCVa9SVSnwaswo3xquW5F1nA2q+jpVXa2qR6hqlaoeqaoXqeoPJpr4LN2N0pg8zc3NcYeQCMyjO+bQHXPojs8OE5FclAO1tbVxh+A9K1euLFzIKIh5dMccumMO3fHZoSUXETE8PBx3CN6zc+fOuENIBObRHXPojjl0x2eHllxERFVVVdwheM+CBQviDiERmEd3zKE75tAdnx1achER4+PjcYfgPdZuJRrMozvm0B1z6I7PDi25iAgRiTsE76mosNsxCsyjO+bQHXPojs8O/Y28zLDkwh17tBQN5tEdc+iOOXTHZ4eWXESEPRZxxwYiiwbz6I45dMccuuOzQ0suIsLnDLNcWLRoUdwhJALz6I45dMccuuOzQ0suIsK6orqzY8eOuENIBObRHXPojjl0x2eHllxERF1dXdwheM8JJ5wQdwiJwDy6Yw7dMYfu+OzQkouI6O/vjzsE73nkkUfiDiERmEd3zKE75tAdnx1achERNnGZO2vXro07hERgHt0xh+6YQ3d8dmjJRUT09vbGHYL3bNy4Me4QEoF5dMccumMO3fHZoSUXEdHQ0BB3CN6zbt26uENIBObRHXPojjl0x2eHllxEhNVcuONzll5OmEd3zKE75tAdnx1achERVnPhjs9ZejlhHt0xh+6YQ3d8dmjJRURYbxF3mpqa4g4hEZhHd8yhO+bQHZ8dWnIRETbOhTurVq2KO4REYB7dMYfumEN3fHZoyUVE2Aid7rS1tcUdQiIwj+6YQ3fMoTs+O7TkIiKqq6vjDsF7Fi9eHHcIicA8umMO3TGH7vjs0JKLiBgbG4s7BO/Zt29f3CEkAvPojjl0xxy647NDSy4ioqLCVLpSW1sbdwiJwDy6Yw7dMYfu+OxwVrEFRWQV8HzgecByYBEwCHQCDwLrgdtVdWgK4jQMwzAMwxMKJhci8g/AO4Fz0qtyFLsQ+Fdgn4hcB1yjqq0RxegFqVQq7hC8Z2jI8tIoMI/umEN3zKE7PjvMW5cvIueLyAPAjcApwPeBfwJOA5YA1cA84DjgRcB/AI8B7wceFZHPisjcqQ2/fJg1q+hKICMPRxxxRNwhJALz6I45dMccuuOzw4kaCtwGjAOvBY5W1Ter6ndV9SFV7VTVMVXtVdVWVb1JVa9S1bOBk4CvA+8G3jv1p1AejIyMxB2C9+zevTvuEBKBeXTHHLpjDt3x2eFEX7dfqaq/KPWAqroN+ICI/BewYrKB+UZNTU3cIXjP8uXL4w4hEZhHd8yhO+bQHZ8d5q25mExikbV/h6re63IMnxgcHIw7BO/ZunVr3CEkAvPojjl0xxy647ND6z8ZEfX19XGH4D2nnnpq3CEkAvPojjl0xxy647PDSSUXEnC0iCzP9Yo6SB+wKdfd8Xl64XLCPLpjDt0xh+747LCkLg4i8mrgQwS9R/Ltq6UeNwnYlOvu+Dy9cDlhHt0xh+6YQ3d8dlh0zYWIvAv4MUFicTdBF9Uf5HhdH32Y5Y/VXLjjc5ZeTphHd8yhO+bQHZ8dllLD8H6C0TjPVtXtUxRPyYjIQuDvgRcDpwJLgRGgCbgWuFZVix7hSkSWAZ8ALgEWAruAXwJXq2p3vv2s5sIdn7P0csI8umMO3TGH7vjssJQ2F0uB/yunxCLk1cC3gTOA+4AvAz8jqGH5DvATEck1quhhiMjxwEbgcuB+4EvAEwTjdfw5TGRy0tfX53AKBsDmzZvjDiERmEd3zKE75tAdnx2WUnPxFFCOgzlsBRelGZgAACAASURBVF4K/C6zhkJEriBIEF4JvIIg4SjE14GjgPeo6jUZx/oiQc3Np4C359rReou4s2bNmrhDSATm0R1z6I45dMdnh6XUXHwfeKGIlFX9v6rerqq/yX70oaodwP+EvzYWOk5Ya3Ex0Ar8d9bmK4F+4A0ikjOLsHEu3Hn88cfjDiERmEd3zKE75tAdnx2Wklx8BvgLcKuInFduSUYeRsPlWBFlzw+XN+dIVHoJGrHOBs7MtbON0OnOsmXL4g4hEZhHd8yhO+bQHZ8dFp1cqOo4wTf6E4DbCWZAHc/xKuYf+ZQjIrOAN4a/3lTELieFy3xDom0Ll6tybRwdHc212iiBrq6uuENIBObRHXPojjl0x2eHpXRFfRnwR2A+waODe4A7c7zuijzKyfEZgkadv1fVPxZRfl643J9ne3p9zmnqnnrqKUQk7+vKK69k06ZNwMHuRZs2bSKVSrFlyxYGBwdpaWmhu7ub9vZ2du3aRVdXF62trfT19dHc3MzY2NiBBj7pY6SXTU1NDA8Ps23bNnp6emhra6Ozs5POzk7a2tro6elh27ZtDA8P09TUlPMYmzdvZmxsjObmZvr6+mhtbaWrq4tdu3bR3t5Od3c3LS0tDA4OsmXLFlKpVKTntHPnzsSdUxzXSVUTd07TfZ06OzsTd07TfZ26uroSd07TfZ1EpKzPaaIp4UVV8248pGAw/fpxwItV9U9F7RQTIvIe4CtAM3COqu4tYp9vAW8D3qaq38mx/VPAFcAVqvrprG13nHXWWefdc889kcQ/U9m1axdHH3103GF4j3l0xxy6Yw7dKXeHjY2NbNiwYYOqNmZvK6XNxUnAjzxILN5NkFhsAc4vJrEISddMzMuzPb1+X66NxSZpRn5SqaKHIzEmwDy6Yw7dMYfu+OywlOSii2BwqrJFRN4HXAM8TJBYdJSw+2PhMmebCuDEcJmzTUZlZWUJb2XkYvbs2XGHkAjMozvm0B1z6I7PDktJLn4GXCQiVVMVjAsi8u8Eg149SJBYdJZ4iPXh8mIROcRL2DPmHGAAyDmNvDXodGfv3mIrmYyJMI/umEN3zKE7PjssJbn4KNAN/J+IrJiSaCaJiHyMoAHnRuBCVc3bxFZEqkRkdTiuxQFUtQW4GVgBvCtrt6uBeuB6Ve3PdVzriurOMcccE3cIicA8umMO3TGH7vjssJQROpuAKoJhtl8iIvvI3bNCVfX4HOunBBF5E8FcIOMEPVXek2O071ZVvS78eSnwKPAkQSKRyTsJesF8VUQuDMudQTAGxlbgI/nimKjVrFEc27dv5+STT447DO8xj+6YQ3fMoTs+OywluaggGIyqLWNdrjk7iprHI0JWhstK4H15ymwArit0IFVtEZFnc3DishcRTFz2FQpMXObzs7FyYfXq1XGHkAjMozvm0B1z6I7PDksZRGuFqq4s5jWVAeeI6ypVlQKvxozyreG6FXmO95SqXq6qR6tqtaoeq6rvmyixAJu4LAoefPDBuENIBObRHXPojjl0x2eHpbS5MCZgzpw5cYfgPaeffnrcISQC8+iOOXTHHLrjs8NJJxci0iAizxCRuVEG5Cu9vb1xh+A96ZHhDDfMozvm0B1z6I7PDktKLkRkloh8SEQeJxhMqhXoFpHHw/WltOFIFA0NPszjVt6sW7cu7hASgXl0xxy6Yw7d8dlhKXOLVBN01fwUQS+Lp4D7w+WKcP2tYbkZh7W5cCc9tr3hhnl0xxy6Yw7d8dlhKTUX/wo0Ar8Dnhk28DwrbBh5EvAb4LlhuRmHtblw57TTTos7hERgHt0xh+6YQ3d8dlhKcnEpwbDaL1fVbZkbwgGoXgE8AvxjdOH5w8DAQNwheE9zc3PcISQC8+iOOXTHHLrjs8NSkosTgD+oas6ZVML1fwCmbQCtcqK2tjbuELxn5cpp7cWcWMyjO+bQHXPojs8OS0kuRoBCdf/1wIycZGN4eDjuELxn586dcYeQCMyjO+bQHXPojs8OS0kuHgJeJSJH5tooIouAVwGbowjMN6qqynI+N69YsGBB3CEkAvPojjl0xxy647PDUpKLrwFHAveLyFtE5DgRqRORlSJyOXBfuP1rUxFouTM+Ph53CN5j7VaiwTy6Yw7dMYfu+Oyw6HEpVPUnInIa8CHgWzmKCPA5Vf1JVMH5RI7J0owSqaiwAWOjwDy6Yw7dMYfu+OywpEGvVPUKEfk18BbgWcA8gplRHwC+p6p/jj5EP7Dkwh17tBQN5tEdc+iOOXTHZ4clj6ipqvcC905BLF5jj0Xc6evrY9GiRXGH4T3m0R1z6I45dMdnh6WM0PlxEXlegTLPFZGPu4flHz5nmOWCr39E5YZ5dMccumMO3fHZYSkPdK4iGKFzIp4HXDnZYHzGuqK6s2PHjrhDSATm0R1z6I45dMdnh1G3FqkCcg6ylXTq6uriDsF7TjjhhLhDSATm0R1z6I45dMdnh1EnF6cDXREf0wv6+/vjDsF7HnnkkbhDSATm0R1z6I45dMdnhxM26BSR27NWXSYijTmKVgLPAI4FfhRNaH5hE5e5s3bt2rhDSATm0R1z6I45dMdnh4VqLhozXkowtXpjjtc5BEN//y/w/ohj9ILe3t64Q/CejRs3xh1CIjCP7phDd8yhOz47nLDmQlUPJB8ikgKuUtVPTHlUHtLQ0BB3CN6zbt26uENIBObRHXPojjl0x2eHpbS5uBz45VQF4jtWc+GOz1l6OWEe3TGH7phDd3x2WMrw39+fykB8x2ou3PE5Sy8nzKM75tAdc+iOzw4n1VtERJaJyBki8rxcr6iD9AHrLeJOU1NT3CEkAvPojjl0xxy647PDkob/FpGLgS8BqwsUrZx0RJ5i41y4s2rVqrhDSATm0R1z6I45dMdnh6UM/30m8FvgCIJp1QW4E/g20Bz+/htgRjb4tBE63Wlra4s7hERgHt0xh+6YQ3d8dljKY5EPA0PA36rqe8N161X17cApwCeB5wM/jTZEP6iuro47BO9ZvHhx3CEkAvPojjl0xxy647PDUpKLs4Bfq+rO7P014OPAo8DVEcbnDWNjY3GH4D379u2LO4REYB7dMYfumEN3fHZYSnIxD8isoxkhGDgrk7sJJi+bcVRURD2S+syjtrY27hASgXl0xxy6Yw7d8dlhKf8RO4H5Wb8fn1WmCrCWjYZhGIYxgykludjKocnEvcBFIrIKQESWAK8EtkUXnj+kUjNyMthIGRoaijuERGAe3TGH7phDd3x2WEpycRNwnogsCH//CkEtxQMi8heCHiNHAl+ONkQ/mDWrpF69Rg6OOOKIuENIBObRHXPojjl0x2eHpSQX3yRoTzEKoKp3A68GthP0FtkFvENVfxB1kIUQkVeJyDUicpeI9IiIisgNkzhOa7hvrlfHRPuOjIxM/gQMAHbv3h13CInAPLpjDt0xh+747LCU4b97gPuy1v0C+EXUQU2CjwJrgT5gB4UH+ZqI/eSufembaKeamhqHtzQAli9fHncIicA8umMO3TGH7vjscMKaCxH5Sjikt0xXQJPk/cAqYC7wDsdj7VPVq3K8Pj/RToODg45va2zdujXuEBKBeXTHHLpjDt3x2WGhxyLvAtYDu0XkOyLyYhEpu9GiVHW9qm5TVY0rhvr67F65RqmceuqpcYeQCMyjO+bQHXPojs8OCyUXxxDUBGwEXg/8GugSkR+LyGtFJIlTgdaIyOtF5AoRea+InC8iBedKsSnX3fF5euFywjy6Yw7dMYfu+OxwwjYXqtoJfAv4VphIvAT4e+BFwGuAYRG5jaDdxa9V9ekpjnc6WAJcn7Vuu4hcrqob8u1kU6674/P0wuWEeXTHHLpjDt3x2WHRvUVUtVdVb1TVVxN0OX0Z8GPgDILJy3aKyJ3ht/1jpybcKeda4EKCBKMeOJWgl8wK4A8isjbfjlZz4Y7PWXo5YR7dMYfumEN3fHY4qTGrVXVYVX+jqpcDiwn+IX8DOJZgSvYnRMQ7K6p6tarerqq7VXVAVR8OJ2b7IsGYHlfl23f37t2ISN7XlVdeyaZNm4CDN8ymTZtIpVJs2bKFwcFBWlpa6O7upr29nV27dtHV1UVrayt9fX00NzczNjbG5s2bDzlGetnU1MTw8DDbtm2jp6eHtrY2Ojs76ezspK2tjZ6eHrZt28bw8DBNTU05j7F582bGxsZobm6mr6+P1tZWurq62LVrF+3t7XR3d9PS0sLg4CBbtmwhlUpFek5pknROcVyn4447LnHnNN3Xqbq6OnHnNN3Xqa6uLnHnNN3X6cQTTyzrc5pokC+Jug2kiDwbeAXwclU9OdKDF/f+jQSNUH+oqq+P6JgnEIw8uldVF+bYfse6devO++tf/xrF281YNm/ezNq1eSuHjCIxj+6YQ3fMoTvl7rCxsZENGzZsUNXG7G2FuqL+QkQuKeXNVPWvqnpFHInFFJJuS5K3S4j1FnFnzZo1cYeQCMyjO+bQHXPojs8OCz0WeRnwu3Dkyo+KyNLpCKoMOTNcPpGvgI1z4c7jjz8edwiJwDy6Yw7dMYfu+OywUHLxeuBO4BnA1QS9Jn4VjndR7gNr5UREqkRktYgcn7X+mSJyWPWDiKwAvhb+mndIcRuh051ly5bFHUIiMI/umEN3zKE7Pjss1BX1RuDG8B/x24A3EnRH/TuC3iHfBb6rqk9NeaQTICIvB14e/rokXJ4lIteFP3ep6gfDn5cCjwJPEvQCSfNa4AMicme4rZdgFtgXA7XA74G8o3SOjo46n8dMp6urizlz5sQdhveYR3fMoTvm0B2fHRY1t4iqtgAfEpGPAC8F3gq8APg48BER+SNBd9TfqGocc4+fBrwpa91x4QuCZOGDTMx64CTgWcA5BO0r9gF/Ihj34vqJRgCtrCw4zpZRAF//iMoN8+iOOXTHHLrjs8OS5glX1XGCAbN+Eba/eAvwZoJBtV4IdIjI91T1Y5FHOnFcVzFBN9Gssq3AYY90wgGy8g6SVcRxJ7urEWK1P9FgHt0xh+6YQ3d8djipcS4AVLVdVT8BrAQuAf4MHA1cEVFsXmHJhTupVByVXsnDPLpjDt0xh+747LCkmotswjk3XkLwmOSMcLW/NhywxyLuzJ49O+4QEoF5dMccumMO3fHZ4aRqLkTkeBH5NPAU8DOCxyK7gHRNxozD5+qrcmHv3r1xh5AIzKM75tAdc+iOzw6LrrkIp1p/JUGvkfMI2i2MA78lmNzsDzE15iwLrCuqO8ccc0zcISQC8+iOOXTHHLrjs8OCNRciskZEvgzsJBjnoRFoI+gpcqyqvkxVfzeTEwtgwjHWjeLYvn173CEkAvPojjl0xxy647PDCWsuRORe4G8JainGgF8R1FL8caJumTMRn5+NlQurV6+OO4REYB7dMYfumEN3fHZYqObiOUAr8BHgGar6ClW9yRKLw+nr64s7BO958MEH4w4hEZhHd8yhO+bQHZ8dFmpzcbGq3jotkXiOz4OdlAunn3563CEkAvPojjl0xxy647PDCWsu8iUWIlIvIs8SkedOTVj+0dvbG3cI3rNx48a4Q0gE5tEdc+iOOXTHZ4cldUUVkWUi8jOgG/grwZDZ6W3nisgWEWmMNkQ/aGhoiDsE71m3bl3cISQC8+iOOXTHHLrjs8OikwsRORq4j2Aa9t8SjMiZOYz2fcBRBBOAzTiszYU7mzZtijuERGAe3TGH7phDd3x2WErNxZUEycNFqvoK4JbMjao6CtxFMOnXjMPaXLhz2mmnxR1CIjCP7phDd8yhOz47LCW5eBHwa1VdP0GZNsDfUT8cGBgYiDsE72lubo47hERgHt0xh+6YQ3d8dlhKcrEY2FagzCjBVOUzjtra2rhD8J6VK2fkyPGRYx7dMYfumEN3fHZYSnKxF3hGgTKrgI7Jh+Mvw8PDcYfgPTt37ow7hERgHt0xh+6YQ3d8dlhKcnE38FIRWZJro4icSDD1+kSPTRJLVVVV3CF4z4IFC+IOIRGYR3fMoTvm0B2fHZaSXPwXUAtsEJEXArPhwJgXLwR+QzDd+hcij9IDxsfH4w7Be6zdSjSYR3fMoTvm0B2fHRY9K6qq3ici/wx8g6ArapqecDkGvFlVH4kwPm8QkcKFjAmpqChp2BUjD+bRHXPojjl0x2eHRScXAKr6PRG5C3gncCawENgP3At8TVUfiz5EP7Dkwh17tBQN5tEdc+iOOXTHZ4clJRcAqroNeP8UxOI19ljEnb6+PhYtWhR3GN5jHt0xh+6YQ3d8duhvnUuZ4XOGWS74+kdUbphHd8yhO+bQHZ8d5k0uRGSp68HDIcNnBNYV1Z0dO3bEHUIiMI/umEN3zKE7PjucqObicRH5koiUNOKmBLxMRB4A3uYWnj/U1dXFHYL3nHDCCXGHkAjMozvm0B1z6I7PDidKLj4H/BPwpIj8QUQuD8eyOAwRmSMiF4jIZ4GngJ8DQ+FyRtDf3x93CN7zyCMzsqNR5JhHd8yhO+bQHZ8d5m3QqapXisi3gY8DlwIXA4hID7CbYNr1WoIeI0cTJCoCPAh8UFV/PLWhlxc2cZk7a9eujTuERGAe3TGH7phDd3x2OGGDTlXdoar/RDAZ2buAXxDUSKwCzgDWEsyU+iDwReBsVT19piUWAL29vXGH4D0bN26MO4REYB7dMYfumEN3fHYoqlr6TiJVBDUWg6q6P/KoPENE7jjvvPPOu+OOO+IOxTAMwzCmhcbGRjZs2LBBVRuzt02qK6qqjqpqhyUWB7GaC3d8ztLLCfPojjl0xxy647NDG+ciIhoaGuIOwXvWrVsXdwiJwDy6Yw7dMYfu+OzQkouIsN4i7jQ1NcUdQiIwj+6YQ3fMoTs+O7TkIiJsnAt3Vq1aFXcIicA8umMO3TGH7vjs0JKLiLAROt1pa2uLO4REYB7dMYfumEN3fHaYiORCRF4lIteIyF0i0iMiKiI3TPJYy0TkeyKyU0SGRaRVRL4sIvMn2q+6unpywRsHWLx4cdwhJALz6I45dMccuuOzw0QkF8BHgXcDpwHtkz2IiBwPbAQuB+4HvgQ8AbwX+LOILMy379jY2GTf1gjZt29f3CEkAvPojjl0xxy647PDopOLcGyLcuX9BAN7zQXe4XCcrxMMCvYeVX25qn5IVS8gSDJOAj6Vb8eKiqTkafFRW1sbdwiJwDy6Yw7dMYfu+OywlP+I7SLyWREpu5lUVHW9qm7TyYwIFhLWWlwMtAL/nbX5SqAfeIOI1E86UMMwDMOYAZSSXFQA/wY8JiK3iMgrRaRyiuKKg/PD5c2qmsrcoKq9wN3AbODMXDunUqlcq40SGBoaijuERGAe3TGH7phDd3x2WEpycQzweuAu4ELgJ8AOEfmUiKyIPrRp56RwuTXP9m3hMmffoFmz8s4BZxTJEUccEXcIicA8umMO3TGH7vjssOjkQlVHVPXGcAzx1cCXCWZV/TDwuIj8XkReJiK+Nj6YFy7zDWmeXp/zao+MjEQe0Exj9+7dcYeQCMyjO+bQHXPojs8OJzu3yFZV/QCwlIO1GZcAPwfaROQqETkmujDLn/b2dkQk7+vKK69k06ZNwMHx4jdt2kQqlWLLli0MDg7S0tJCd3c37e3t7Nq1i66uLlpbW+nr66O5uZmxsTE2b958yDHSy6amJoaHh9m2bRs9PT20tbXR2dlJZ2cnbW1t9PT0sG3bNoaHhw+M+pZ9jM2bNzM2NkZzczN9fX20trbS1dXFrl27aG9vp7u7m5aWFgYHB9myZQupVCrSc+ru7k7cOcVxnebOnZu4c5ru69Tf35+4c5ru6zQ4OJi4c5ru6zR//vyyPqcJH9uoqtOL4Bv/e4AdQCrjNUxQu1Hj+h4lxtMIKHBDifv9V7jfB/Js/1q4/R05tt3x7Gc/Ww03HnroobhDSATm0R1z6I45dKfcHZ533nkK3KE5/mdO+hGGiJwpItcCOwm6atYDXyUYa+LNwGPAv4QJhg88Fi7zjbd6YrjM2Sajvt46kbhy6qmnxh1CIjCP7phDd8yhOz47LCm5EJEGEXmniGwm6D3xJqAZ+CfgGFV9n6o+pKrXAc8CbgdeFXHMU8X6cHlxdrsREWkAzgEGgHtz7WxTrrvj8/TC5YR5dMccumMO3fHZYSmDaH2XoJbiGoJv8dcDZ6rqOlX9rqoOZpZX1XHgDmBBdOG6IyJVIrI6HNfiAKraAtwMrADelbXb1QQ1M9eras7pT23KdXd8nl64nDCP7phDd8yhOz47LKXm4nKgA/h/wDJVvUxV7y+wzx3AJyYZW9GIyMtF5DoRuQ74ULj6rPQ6Efl8RvGlwKPAbTkO9U6gE/iqiPxSRD4tIrcTjAC6FfhIvhis5sIdn7P0csI8umMO3TGH7vjssJTBGS5R1ZtLObiq3k3w+GSqOY3gEU0mx4UvgCeBDxY6iKq2iMizCRKiS4AXAbuArwBXq2p3vn2t5qJ0RsZS3N3SxR8f7mB7Vz9Do+PU/vXPrFxUzwtOWcI5xy+iepavPZvjw+dvO+WCOXTHHLrjs8Oik4tSE4vpRFWvAq4qsmwrIBNsf4qglqYk+vr6St1lRnPzIx18/55WOnqG2D84yuDIOGPjKWZVVtDydB9/ad3Lknm1vOmsFVy8Zknc4XrF5s2bWbt2bdxheI05dMccuuOzQxtWMiKst0jx/Oj+Nq67p5WO/UEf6Xl1VRzVUIugKELf8BhdfSN09Y3whVu2sqd/hNc9Z3nMUfvDmjVr4g7Be8yhO+bQHZ8dltKgMyUi4wVeYyKyV0TuFpEPiEjNVAZfTgwODhYuZHDzIx1cd08r7d0DzKur4hnz65hXV0VlhTAyMkJlhRyyvr17gOvuaeWWLf6OVDfdPP7443GH4D3m0B1z6I7PDkt5oH0n8BDBI4UUQTuG+8NlKlzfBLQTdEP9HHD3TJlFtKZmxuRRk2ZkLBU8Ctk/xIL6GubVVSFy8AlVVXXVgZ9FgiRjQX0NHfuHuO6e7YyM2eRwxbBs2bK4Q/Aec+iOOXTHZ4elJBevIxiN88fA8ap6nKqeparHAceH6+cCFwGLge8BpxP0Lkk8o6OjcYdQ9tzd0kVHT/AoZG7t4U/kxsbGDluXLtexf4h7WrqmNsCE0NVlnlwxh+6YQ3d8dlhKcvFZYK+qXqqqbZkbVLVNVS8FuoHPajBF+dsJum++MrJoy5jKyiTNPj81/PHhDvYPjh5WY5GmsuJwh+kajP2Do9z0cMd0hOk9c+bMiTsE7zGH7phDd3x2WEpy8QLglgJlbiHowpkeROtOYOXkQvMLDeYYMSZge1c/gyPjzKnJ3Y44n8M5NbMYHBmndU/O8cuMLKwWzR1z6I45dMdnh6UkFw0Ejz0mYl5YLs3ekiPyFEsuCjM0Ok5KoSJPR2Alt8MKgZTC4Mj4FEaXHFIpa5viijl0xxy647PDUrqiNgOvFZFPqerO7I0isgx4LcHol2meAexxC9EP7LFIYWqrKg8kCpU5EoyKity5bjohqas2x8Uwe/bsuEPwjtHxUe7ddS+3td3Gkz1P0jfcx5ymORw791guXH4hZx59JlWVVYUPZBzA7kN3fHZYSnLxBYL5RDaJyDUEI2/uJmi8eS7BDKhHAF8EEJFZwPOBu6IMuFzxufpquli5qJ6Wp/voGx5jXt3hH9TjY+PMqjz8luwbHqOuupIVC2dExyNn9u7dy/z58+MOwxtub7udGx+9kc6BTnpGehgcG2Q8NU5lRSXb929n0+5NLK5fzOtWv44Lll8Qd7jeYPehOz47LGWEzh+KyFLgkxw+X4gAY8BHVPWH4bojgI8D90URaLljXVEL84JTlvCX1r109Y0wt3bWYY06q6oOTzhUlf2DoyyaU80lp9hIncVwzDHHxB2CN/x060+58dEb2T0QjKMyt3oui+oWgQIC/aP97Bnaw56hPXztga+xd2gvr1rly0TP8WL3oTs+Oyxp4gZV/RxwEnAl8AuCKdV/SZBErFbVz2SU7VLVb6rqgxHGW7YMDQ3FHULZc87xi1gytxaAnqHDu50Ojwwfti5dbsm8Ws4+ftHUBpgQtm/fHncIXpCusdjZv5O5NXNZOmcpc2vmUllRyejIKJUVlYes39m/kxsfvZH1bevjDt0L7D50x2eHJQ//rarbCWovjAx8fjY2XVTPquBNZ6/gC7dspb17AOCQGoza2toDZVWVnqEx9vaPsHR+HZedvdImMSuS1atXxx1C2TM6PnqgxmJ+7XzmVh/aVj3zXhSRA9t3D+zmxuYbOXfpudYGowB2H7rjs8NShv8eF5EfFi45M7GJy4rj4jVLuOzsFSydP5v9g6M81T3I/sFRxlPKQP8A4yk9ZH2QWKzgopMXxx26Nzz44IyoLHTi3l330jnQCUBD1eEzGg8MDhy2Ll1ud/9u7uuYEU97nbD70B2fHZZSc9ELtBUsNUPxebCT6eZ1z1nOwvpqvv/nYCjw/YOj7OkbDnqFDPRTV13JojnVLJlXy2Vnr7TEokROP/30uEMoe25ru42ekR7mVs/NOaBbrprIdA1Gz0gPtz55K+cuPXc6QvUWuw/d8dlhKcnFA8DJUxWI7/T29sYdgldcvGYJjScdxT0tXdz0cAete/rp6u5l0fwGViys55JTlnD28YvsUcgk2LhxI+vWrYs7jLLmyZ4nGRwbDBpv5mCgf4DZ9YcnGPVV9ewd2ktbr33PKoTdh+747LCU5OKzwG9E5CJVLTRS54yjoeHwqlVjYqpnVdB40lE0nnRU3KEkCl8/jKaTobEhUpqiQnInr7kSC4AKqUBRBkdtFuRC2H3ojs8OS/laeBRwE/AHEfmpiPy7iLxJRN6Y/ZqiWMsaa3PhzqZNm+IOIRGYx8LUzqqlQipIae4REAcGDm9zAZDSFIJQV1U3leElArsP3fHZYSk1F9dxoPc3rwhfhOvSSPj7D6IIzieszYU7p512WtwhJALzWJhj5x7L9v3b6R/tZ27N4bMazK7LXXPRP9pP3aw6ljcsSv2lBQAAIABJREFUn+oQvcfuQ3d8dlhKcnH5lEWRAPJ90zGKp7m5mZNPtmY9rpjHwly4/EI27d7EnqE9NFQ3HNaoc2hoiNq62kPWqSo9Iz0srF3I8499/nSG6yV2H7rjs8NSRuj8/lQG4juZ/eKNybFy5YyYQHfKMY+FOfPoMzlq9lHsGdpD72jvYeNcVNdUH7ZP72jQaHtx/WLOWHLGtMTpM3YfuuOzQ2uKHxHDw4ePLmmUxs6dh82HZ0wC81iYqsoqLn3mpSyevZjuoW56RnoOmdk4c66gdI1F91A3i2cv5tLVl9oAWkVg96E7PjsseYROETkSeCXwTKBeVd+asX4l0KSqM64pda55MYzSWLBgQdwhJALzWBwXLL+AvUN7D4zU2TMcjHtRX1VPZUUl46lx+kf76RnpAeCY+mO49JmXcv7y82OO3A/sPnTHZ4clJRci8hbgq0AtBxtvvjXcvBj4M/BPwHcjjNELxsfH4w7BewYGBrydAbCcMI/F86pVr2JB7QJ+1Pwjdvfvpmekh71Dew/Milo3q46FtQtZXB/UWFhiUTx2H7rjs8OikwsRuQj4FvAQwcRlLwDent6uqg+LyCPAy5mByUWuUf6M0qiosKd0UWAeS+OC5Rfw3KXP5b6O+7j1yVtp622jZ6CHubPnsrxhOc8/9vmcseQMexRSInYfuuOzw1JqLv4d2AWcp6o9IvKsHGUeAs6KJDLPsOTCHXu0FA3msXSqKqs4d+m5B4b07urqYtEim4XXBbsP3fHZYSlp0bOB36pqzwRldgBL3ELyE3ss4o4NRBYN5tEdc+iOOXTHZ4elJBfVQH+BMkcAM/K/rM8ZZrlg3xSjwTy6Yw7dMYfu+OywlOSiFSg00PkZwGOTjsZjrCuqOzt27Ig7hERgHt0xh+6YQ3d8dlhKcvEr4Lki8upcG0XkcuBvgJ9FEZhv1NXZXAOunHDCCXGHkAjMozvm0B1z6I7PDktJLj4HtAE/EpH/JWy4KSLvDn//FrANuCbyKD2gv7/QEyOjEI888kjcISQC8+iOOXTHHLrjs8NShv/uFpHzCCYly6y9+Gq4vAu4VFVn5H9Zm7jMnbVr18YdQiIwj+6YQ3fMoTs+OyypE62qtqlqI3Aa8A7go8C/AH+rquepanv0IfpBb29v3CF4z8aNG+MOIRGYR3fMoTvm0B2fHZY8/DeAqj5EMKZF2SAiy4BPAJcACwnG5PglcLWqdhd5jDuA8yYoUqeqQ7k2NDQ0lBSvcTjr1hVqL2wUg3l0xxy6Yw7d8dnhpJKLckNEjgfuAY4iaHjaDDwHeC9wiYico6p7Sjjk1XnWj+XbwWou3Nm4caPXf0zlgnl0xxyWjo6M0H/vvfTecgsjrU/Su2cPDQsXUr3iWBouuoj6M89Eqg+fbdbIj8/3Yalzi1QBLyP4xz0fqMxRTFX1LRHEVgpfJ0gs3qOqBxqUisgXgfcDnyJjqPJCqOpVpQZgNRfu+PpHVG6YR3fMYWn03nYbe6+/gbHO3Yzv70EHB5mlylBHB8NPPMHAxo3MWryEBa//RxouvDDucL3B5/uwlLlFjgFuAVYTTFqWDwWmLbkIay0uJhiH47+zNl9JMJHaG0TkA1PZ2LQce4uMjKW4u6WLPz7cwfaufoZGx6mtqmTlonpecMoSzjl+EdWzymfs+qamJk499dS4w/Ae8+iOOSye7p/8hO4bfshoRweIUDl3LhVHHsnQ8DC1NTWk+vsZ27OXsa49PP2VrzK2Zw/zX/OauMP2Ap/vw1JqLr5AMM36j4BvA08xwWOCaSQ9TeHNqprK3KCqvSJyN0HycSZwWzEHFJHXEkwfPwI8CtyuqhOOklVu41zc/EgH37+nlY6eIfYPjjI4Mk5KoUKg5ek+/tK6lyXzannTWSu4eE15jNi+atWquENIBObRHXNYHL233RYkFu3tVC5YQEVDw4F5lmrq6pB0stHQQKq3l9H2drpv+CGzFi2i4YILpj3e8bEUO5q7eeKBTvZ1DjI2Ms6s6kqOOKqO4551FMtWz6eyjL5w+XwflpJcXAzcqar/OFXBTJKTwuXWPNu3EcS+iiKTC+DHWb93isi7VPWn+XYopxE6f3R/G9fd00rH/qDt6by6Ko5qqKVCIKXQNzxGV98IXX0jfOGWrezpH+F1z1kec9TQ1tbGiSeeGHcY3mMe3TGHhdGREfZefwOjHR1ULlhA5dy5h2wfGRmhtqYG4ECSATDa0cHe629gzrnnTmsbjCcefJqm9Tvo2zfM8MAoYyMpVBURobujn52P72fO/BpObVzGcacdOW1xZZKd/PT1DjCnYXbZJj8TUUqUtcB9UxWIA/PC5f4829PrjyjiWL8CXgIsA+oIHgF9Otz3f0Xkknw77tixAxHJ+7ryyivZtGkTcLB70aZNm0ilUmzZsoXBwUFaWlro7u6mvb2dXbt20dXVRWtrK319fTQ3NzM2NsbmzZsPOUZ62dTUxPDwMN+/9QG+d1cLT+3pZ061sKRhFnWVCppieGQYEaWaMZ4xv466inHauwf4xm2PcsuW3WzevJmxsTGam5vp6+ujtbWVrq4udu3aRXt7O93d3bS0tDA4OMiWLVtIpVKRntOePXtyntO2bdvo6emhra2Nzs5OOjs7aWtro6enh23btjE8PExTU1NOL3GfU77rNJXnVFdXl7hzmu7r1NPTk7hzivo6da1fz+COHagq47W1jIyOMjY+zvDwMKlUitT4OAoMDAwA0D8wQEVDA+Pj44x1dPDoT/5v2s5p8/on2fB/W3j6qV569w1SVVNJVb1yxOLZVM9RZlVX0rdvkM4ne/jTzx7jvj88Nu3X6Zaf3ctvr9nMbTc8zBObu9jdup/+PaPsfnI/LQ92suHHj/LTz9/HI39+smzuvaGhnJ0nARBVzbvxkIIi9wHbVfUfitphmhCRbwFvA96mqt/Jsf1TwBXAFar66Um+x7sJRh59UFUPm2peRO4488wzz/vzn/88mcNHxshYisuvvZ+Hd/Ywr66KeXWFJ1PbPzjK/sFRTlk6l2sve06sbTDa2tpYvjz+GhTfMY/umMPC7PrYx+i9fT0Vs2cfVmsBQc1FdY6aifGeHlIDAzRccD5H/8d/THmcTzz4NPf/5gl69gxRN6eK6rpZBx7dZKKqjAyOMdg3ytyFtZzx0uNYuXZ6ajAeuaudpjt20Ncd1IDXzJ5FVe0sRkdHqKqqZnRojOGBoBVCunZlzXOXTktsE9HY2MiGDRs2hONfHUIp/0n+C3ipiJwcWWTRkK6ZmJdne3r9Pof3+A5B+5LTRCRnt5CKivirqu5u6aKjJ8gk59YW98QrXa5j/xD3tHRNWWzFUFtbG+v7JwXz6I45LMxI65Po4CAV9fU5t0uez8SK+np0aIiRJ9umMjwgeMzQtD74p103p4qa2VU5EwsIHt3UzK6ibk4Vfd3DPLR+B+NjqZxlo+SJB5+m6Y4d9OwZomb2LBoW1lIzu4qKCqGysoKKiiCuYP0sevYM0XTHDrZvfnrKY3OhlDYXncBvgHtE5CvARvL8w1bVOyOIrVjSs7Dma/mSfnCar01GQVR1SER6Cbrf1gNlOajFHx/uYP/gKPPq8v8BZSMizKurYv/gKDc93EHjSUdNcZSGYSSB1NAQqgqlfrGqqEBTKVKDg1MTWAY7mrvp2xfUBlTXFffvrrpuFsMDY/TtG2bHY90cu2bhlMWXK/nJRzr5AQ4kP8vXLCzbNhilJBd3EHQzFeBj4c/5yDX+xf9v78zj5CrLfP99eu/0kk46G0nIRiAdAxIIQ8IS0xASGRmBGdGrLJqZq3JnHAXHGR31ItFxRsc7CzDOXNFxjMOijijLjAuBYAIqiZeEMCgJGLKRhCSkO0nvW9Vz/3hPhe5KVVdVTlWdcyrP9/Opz+l633Pe85xfnerz1Ls8T6H4mbddKSJlw1eMeL0MlwE9wMZTPYGIzMM5Fp1Ayp/38XjhPdxM7DrSTe9AjEkNuf3qqq+uoK2rn91twS6nHW38zhiFoQHYtQG2PQZtOxnbfQzqmqB5Dsy/FmYvgwoLXpQLdi9mpqymxv2Iiceh/OR/+Zruf2I8jpSVUVaEFXY7nz9Mf88g1WNSD4Wkwj3EK+jvHmTnlsMFdS4yOT/x+MmP2WI6P37Ixbn4AqM7FIGgqq+KyFrcipCPMDIr6+dxPQ33Do9xISIt3rHbh5XNBo6ravvw9kVkIvAt7+13VTXl8tuKiuCDnfYNvrncNBcSq0h6B2KFMSxLmpqymXNrjGD7j2DTvdD5OvQehcFequIx6CiHIy/D3o3QOBUu/jC0XBO0tZHB7sXMVM2aSf/OncS7u1POuShP4XAAxLu7kZoaqmYWfk6LW24aZ8zY6pyOq6ypoLdzkONvFLZ3JZPzk0rDYjo/fsglK+rqAtrhlz/Bhf++R0SW42JTLMbFwHgF+GzS/tu87fBPcxnwNRH5ObATaAdmAO/Azdt4DvhkOgMGBgb8X4VPairLTzgK5Tk4GAmHpLaqmB1OJ3Po0CEaU/yTMtKweY1zLDoOuPe1TVA/mf6BIWqqKmCgC7oOu9dTX4TuN2DRqiAtjgx2L2amYcUKejZvZqitfUR8iwSDQ0MnPRxVlVhHBxXNzTSsXFlwG4cGYt5y09yOE3G2DvYX9gdXJudnaGgwpYNRLOfHD+EcrMkRVX0VuAhYg3MqPgGcBdwNLMkyr8hmXHyLycC7vDauBl4EPgZcpqppJ4VWV+fmGReC2RPqqK0qp6s/t9hmXf1D1FaVM6s59cSsYmGz83Mg0WNx7DXnVDTNhJomKKugqroayirc+6aZrv7Ya27/7T8O2vJIYPdiZuqWLKFi0mRQJZ4it1KqlSLxzk5QpWLyZOoWLy64jRVV5YgIWS6KPIGq6yGorC7sD65Mzk8qDaF4zo8fRnUuRORtIpL1t0xEzheR9/s3K3dU9TVV/UNVPUNVq1R1pqreniojqqqKqkpS2YuqukpVz1PVZlWtVNXxqrpUVf9JVUftmugtwuSkTLz93CknJmdmu8RYVU9MAr363GAjdb7yyinPuT29GBp4s8eirtk5EcP+O42YLyDi6uua3f6/utcdb4yK3YuZkaoqxt9yM5VTphBrbyfW0THi/07/sPsw0WMRO9pO5ZQpjL/l5qIE0GqaVEtFVRmDfbn94BrsG6KiqoyxEws7LyST89PXlzo4Y7GcHz9k6rn4GbBqeIGIfEpE0vUEXM+b8xNOK+rSLMcqJpedNYEpjW4yZ0eWX6bEflPG1nDpWRMKZls2RDWGftHZtcHNsQCoPnkF9phUE+US+3UcgF3FXMwVTexezI6G5csZd/NNVE6bRuz4cQb373dORixGTU0NGosR6+hw5cePUzl1GuNuvqloob/nXDCJ6jGV9PcM5fSDq79niOq6SuZcWNjVc5mcn3RpJYrl/Pghk3ORqrOmhuyiXZ5WhCHlelVFGR+4dBZTxtbQ3t0/ag9GoseivXuAKWNrWHXp7MCTmCUiwxkZ2PaYm7xZO7LHIkG3FxFxBIkejN6jsO3RIhgZbexezJ5x73kPE2/7GDULFlDR3Ey8p4fB116j79VXGXztNeI9PVQ0N1OzYAETb7+tqEnLpreMo77JDVkP9Gb3gyuxX31TNdPnjSuYbZDZ+elJ8V0upvPjh+CXOJQIYUm5vnLBFNq6B07kFkkMedRXV4zILXK8dxCAaeNqWXXpLFa8ZXLAlkc7vXBRadsJg71Qn/ozqxszJvVx1fXQcwTadxXQuNLA7sXcaFi+nPqlS+netInOtWsZ2LOXeG8vZbW1VM2cQcPKldQtXlzUXCIA5RVlnHfF9BMROoGsI3S+9YrpBY8hkXB+ejsHGOgdOinOxZgU3+ViOj9+MOciT4Sh5yLB+y6eQXNdFd9+9k0Ho62rf8SqkAn1VSd6LMLgWID7tWj/1LNgsAc0DpJ6vLW7pye1gyHl7riBYOOZRAG7F3NHqqqoX7qU+qVLgfBoOGfhRHo7B06E1+7vGToRXttNjGREeO3G5hrOa51elNDfmZyfnp6eEw5GEM6PH8y5yBNh6blIsHLBFFrnTeKXrx7hp78+yO42F2ArsSrk6nOncOlZEwIfChlOGP4RRYLKMSBloDGQk7/CaXsuNOaOqwp+flDYCdu9qAMDdG/cSOcTTzCwew/xvj7KamqomjWThhUrqFuypOi9ApkIk4YLlk6jtqHKORjH+unvHqS3c/BEVtSKqjJqG6uob6rmrVcUx7FIMJrzU1tbSzyugTk/fjDnIk90dXUFbcJJVFWU0TpvUmRCer/wwgucf/75QZsRfprnuABZA11uHkUSPb29qSd19ndBZS2Mn10EI6NNmO7FznXraL/vfoYOHyJ2vAPt7T3xUOzfuZOezZupmDyF8TffRMPy5UGbe4IwaQjuIT7z3Gb2vXyUnVsOc/yNXgb7Y1RWlzN2Yi1zLpzE9HnBpDRP5/zEYjHKy8sDdX5OlWyci9BF5QwjYVgtEnUWLFgQtAnRYP61LvJm12G3CiRp/Li2NkX4d1XoOwb1k2D+dUUyNLqE5V48+h//wdH7H2Dw4EEQobyxkbKJE10+j3iceHc3Q23tDB1p442772Gora2oEyZHIywaDqe8ooyZC5pDGdUylfMz0B+jKgTOz6mQjXOxWkRWJxeKSHijdwRAGOJcRJ0dO3bQ0tIStBnhZ/YyaDjDORf9x0/qvejr66c2Oatnv5c8uHEqzH5bkQyNLmG4FzvXrXOOxf79lI8ff3IUzPJy52w0NBDv7GRw/36O3v8AFRMmFG2p52iEQcOokez8bN++PbIaZuMCSY6v05IwROiMOtOnTw/ahGhQUQWLb3WOQneb65EYtoxtRFS/RI9FT5uXY+RWS2KWBUHfizowQPt99zN48CDl48dT3tg4aqrw8sZGysePZ/DgQdrvux8NQTqCoDUsBaKs4ajOhaqWncIrvCHDCsjg4GDQJkSeI0dSJpw1UtFyjXMwms6E3mNwbI/bxocYGhyE+NDI8rFnuv1b3hG05ZEg6Huxe+NGhg4fAhHKspwsXtbQACIMHTpE96ZNBbYwM0FrWApEWUOb0Jkn0mUANLKnvr4+aBOixaJVUDcRfvV1F3mz9yj0HKEyHoOycjd5s37Smz0W5lhkTdD3YucTTxA73jFqj0UyiR6M2PHjdK5de2JJaFAErWEpEGUNzbnIE9mGljXSY70/p0DLNTB3hQvpve1RaN/FUPcxquqa3KqQ+de5ORY2FJITQd+LA7v3oL29bvJmDpTV1RFrb2dgz94CWZY9QWtYCkRZQ3Mu8oQ5F/6Jx+NBmxBNKqrg7KvcC3hj/36mTZsWsFHRJuh7Md7X5/6nlOW4MqCsDI3HiYdggnnQGpYCUdYwGmtaIoANi/gnVahbI3dMR/8ErWFZTY0bDsn14RKPI2VllKVJeFVMgtawFIiyhuZc5Ikod1+Fhfb29qBNKAlMR/8ErWHVrJlIbS3x7txCtce7u5GaGqpmziiQZdkTtIalQJQ1NOciT9hSVP9MnTo1aBNKAtPRP0Fr2LBiBeVjG1368hxShcc6OigfO5aGlSsLbGFmgtawFIiyhuZc5Im+vr6gTYg8u3ZZts58YDr6J2gN65YsoWLSZFAlnmVSxHhnJ6hSMXkydYsXF9jCzAStYSkQZQ3NucgTUR4bCwtRjUQXNkxH/wStoVRVMf6Wm6mcMoVYe/uoPRiJHovY0XYqp0xh/C03hyKJWdAalgJR1tCcizwRxsRlUWPr1q1Bm1ASmI7+CYOGDcuXM+7mm6icNo3Y8eMM7t/vnIxYDFVFYzFiHR2u/PhxKqdOY9zNN4Ui9DeEQ8OoE2UNbSlqnohysJOwcOGFFwZtQklgOvonLBqOe897qGhupv3+Bxg6dIjY8ePE2ttRb1WI1NRQ0dxMxeTJjL/l5tA4FhAeDaNMlDU05yJPdGY5LmqkZ/PmzSxatChoMyKP6eifMGnYsHw59UuX0r1pE51r1zKwZy/x3l7KamupmjmDhpUrqVu8OBRDIcMJk4ZRJcoaigV/8o+IrF+2bNmy9evXB22KkW+GBmDXBtj2GLTthMEeqBwDzXNc6vPZyyz6pWEYpyWtra1s2LBhg6q2JtfZnIs8YXMu/LNly5agTRjJ9h/BAzfA45+Bl38Cr2+FN7a77cs/ceUPvtvtFyJCp2MEMQ39Yxr6J8oa2rBInrA5F/5ZuHBh0Ca8yeY1sOlelxAMoLYJ6ieDlIPGYKALug6711NfhO43XCKxEBAqHSOKaegf09A/UdbQei7yRE9PT9AmRJ7t27cHbYJj+4+cY3HsNedUNM2EmiYoqwARt63xymub3H6b7oXtPw7aciBEOkYY09A/pqF/oqyhORd5oqamJmgTIs/s2bODNsHNsUj0WNQ1OyciXcprEVdf1+z2/9W97viACYWOEcc09I9p6J8oa2jORZ7o7+8P2oTIc+DAgaBNcJM3O193f1ePze6YxH4dB1zq84AJhY4RxzT0j2nonyhraM5FnqisrAzahMgzfvz4oE1wq0J6j7rhjnQ9FskkejB6j8K2RwtrXxaEQseIYxr6xzT0T5Q1NOciT8RisaBNiDyhmLfSthMGe6Eqxwm61fXuuPbgcwGEQseIYxr6xzT0T5Q1NOciT0i2v3KNtJSVheB2HOwBjbtVIbkg5e64gdxSZBeCUOgYcUxD/5iG/omyhrYUNU+Yc+GfUAwtVY4BKXPLTSWHr4fG3HFVdYWzLUtCoeMwBmODbHx9I+v2rmNPxx76hvqoqahhZuNMls9YzpIzllBZHi6bw6ZhFDEN/RNlDUvGuRCR6cAXgKuBZuB14BHg86p6NId2xgOfA64HzgDagJ8Cn1PVfemOs2ER/3R1dTFhwoRgjWieA0dednEsapqyP66/CyprYXzws7tDoaPHU3uf4sFtD3K45zAdAx30DvUS1zhlUsau47vYcmgLk+sm876W93HljPDkxQiThlHFNPRPlDUsCedCRM4CfglMAh4FtgMXA7cBV4vIZaralkU7zV475wBPAd8FWoA/BK4RkUtUdWeqY6PsYYaFUHyJ5l8Leze64FjVY7Ob1KkKfcegfhLMv67wNmYgFDoCD73yEA9ue5BDPYcAaKxqZELtBMqkjLjG6R7spq2vjba+Nr76/Fdp72vnhnNuCNhqR1g0jDKmoX+irGF0B3RG8i84x+Jjqnq9qv6lql4J/CMwD/jrLNv5G5xj8Q+qutxr53qckzLJO09KbCmqf/btS9sxVDxmL4OGM9zf/cezOyaxX+NUmP22wtiVA2HQMdFjcaD7AI3VjUyrn0ZjdSPlZeWICOVl5SPKD3Qf4MFtD/KzvT8L2nQgHBpGHdPQP1HWMPLOhddrsRLYDfxzUvWdQDdwi4iMOhguIvXALd7+q5OqvwrsAd4uInNSHV9bW5ur6UYSc+fODdoEl4Rs8a3OUehucz0S6ZL7JXosetrc/hffGookZkHrOBgbPNFjMa5mHI1VjWnnJIkIjVWNjKsZx6GeQzy4/UEGY4NFtvhkgtawFDAN/RNlDSPvXABXeNu1qhofXqGqncAvgDHAkgztLAFqgV94xw1vJw48nnS+EXR3B79KIOr85je/CdoER8s1zsFoOhN6j8GxPW4bH3IORXxoZPnYM93+Le8I2nIgeB03vr6Rwz2HAWiobMjqmMR+h7oPsengpoLZli1Ba1gKmIb+ibKGpeBczPO2r6Sp/623PaeQ7VjiMv+cf/75QZvwJotWwZX/G6YudHMpBrvh6G5o+63bDna78qkL4co7QpO0DILXcd3edXQMdIzaY5FMogejY6CDJ/c8WWALMxO0hqWAaeifKGtYChM6EzGa0w2QJ8ozTf331c62bdtobW0d9QSrVq1i1apVJ96vWbOGNWvWjHrMwoULueuuu06837p1K7fffvuoxwCsX79+xPtMtgHcddddI7Lw3X777WzdunXUY/J5TZ2dnTQ0pP+lW/RrarkG5q7gia/fQf9vv8+0Wqgph74Y7O+N8/SRGFuOHmXo377CwoVrQ/M5pdKxmPfe7L+cTe9QLxNq3WS0l19+OeMxM86cQV1NHe197ezt3Fv0ey+ZZA2j+H3KRKGvKaFhKV1TgmJdU2dnJ5s3bx5RFqZr2rFjR9q6UnAuQsGxY8fYsGHDqPvMmzePVatWsXnzZhYtWsSzzz6b8Zi+vj66urrYt28fc+fO5bnnnst4DMDevXtPJFPr6+vL6pjnnnuOc889lx07djB9+nQ2btzIpk2jd1GfDtf00AvH+Po3Ro+8GbVrKuTn1PyxZmIaIz4UJ0aMrs6ujMd0d3dTXVNNLB6jq7+Llza+FKprKsXPya7JrsnvNY32Y7AUnItEj0K6LFOJ8mOFbKepqYn58+ePeoJLLrkEgEWLFp14n+lX3cKFC6mvr6elpQWAiy66iGXLlo16DMCMGTNGvM/mmIsuuoiKiooT51qyZEnGbK/5vKZMPRdRvKZMFOKaUulYzGtqHNNIeW85ZRVllJeVU9+Qeciwrq4ORd3+1fWBf07JGtq9x4m2s72mhIaldE0JinVNnZ2dob6mHTt20NnZmbJONN1M+IggIh8EvgF8XVVvTVH/OG41yVWqum6Udq4CnsBNDH17ivp7gQ8DH1TVbybVrV+2bNmy5C45wzgdWf3L1ax/bT21FbU0VjdmfVxHvwuy1XpmK6svXV04Aw3DyAutra1s2LBhg6q2JteVwoTOxML4lSIy4npEpAG4DOgBNmZoZyPQC1zmHTe8nTKcgzL8fCOw1SL+efHFF4M2oSQIWsflM5afmJyZ7Y8XVT0xCfSqmVcV2MLMBK1hKWAa+ifKGkbeuVDVV4G1wCzgI0nVnwfqgPtU9cTTX0RaRKQlqZ0u4D5v/9VJ7fyp1/7j6SJ0WpwL/5xzTqYFPUY2BK3jkjOWMGnMJAA6B1N3mSaT2G9y3WQWT1lcMNuyJWgNSwHT0D9R1jCNzEE3AAAVWUlEQVTyzoXHnwCHgXtE5BER+ZKIPAV8HLe09LNJ+2/zXsl8xtv/z0RkndfOI8DdXvvJzssJLEKnf/bu3Ru0CSVB0DpWlldy4/wbmTxmMkf7jo7ag5HosTjad5TJYyZzY8uNoUhiFrSGpYBp6J8oa1gSzoXXe3ERsAZYDHwCOAvnFCzJJq+I104bcAlwDzDXa2cx8C1gkXeelFRVBR+ZMepMnjw5aBNKgjDoeOWMK7lx/o1MrZtKR38H+7v209HfQSweQ1WJxWMjyqfWTeXG+TdyxYyUMeqKThg0jDqmoX+irGEprBYBQFVfwyUYy2bftJF9VLUdl0vktlzOPzQ0lMvuRgqOHTtGY2P2EwCN1IRFxxvOuYHxNeP5zvbvcKj7EB0DHbT3taMoglBbUUtzTTOT61yPRVgcCwiPhlHGNPRPlDUsGeciaMrKSqITKFAyLZUysiNMOl4540qWTlvKpoObeHLPk+zt3EvvYC+1lbXMaJjBVTOvYvGUxaEYChlOmDSMKqahf6KsoTkXhmEUlMrySi6fdjmXT7s8aFMMwygS9nM7T8Tj8cw7GaPS19cXtAklgenoH9PQP6ahf6KsoTkXeaKiwjqB/NLUlCn9i5ENpqN/TEP/mIb+ibKG5lzkiYGBgaBNiDyHDh0K2oSSwHT0j2noH9PQP1HW0JyLPFFdXR20CZEnOYa+cWqYjv4xDf1jGvonyhqac5Enent7gzYh8rzyyitBm1ASmI7+MQ39Yxr6J8oamnORJ+rq6oI2IfKcd955QZtQEpiO/jEN/WMa+ifKGppzkSfSpZ01smfz5s1Bm1ASmI7+MQ39Yxr6J8oamnORJxoaGjLvZIzKokWLgjahJDAd/WMa+sc09E+UNTTnIk9Yz4V/ouylhwnT0T+moX9MQ/9EWUNzLvKE9Vz4J8peepgwHf1jGvrHNPRPlDU05yJPdHV1BW1C5HnhhReCNqEkMB39Yxr6xzT0T5Q1NOciT9hqEf8sWLAgaBNKAtPRP6ahf0xD/0RZQ3Mu8oTFufDPjh07gjahJDAd/WMa+sc09E+UNTTnIk9YhE7/TJ8+PWgTSgLT0T+moX9MQ/9EWUNzLvLE4OBg0CZEniNHjgRtQklgOvrHNPSPaeifKGtozkWeKC8vD9qEyFNfXx+0CSWB6egf09A/pqF/oqyhORd5QlWDNiHyWO9PfjAd/WMa+sc09E+UNTTnIk+Yc+GfeDwetAklgenoH9PQP6ahf6KsoTkXecKGRfwzZsyYoE0oCUxH/5iG/jEN/RNlDc25yBNR7r4KC+3t7UGbUBKYjv4xDf1jGvonyhqac5EnbCmqf6ZOnRq0CSWB6egf09A/pqF/oqyhORd5oq+vL2gTIs+uXbuCNqEkMB39Yxr6xzT0T5Q1NOciT0R5bCwstLS0BG1CSWA6+sc09I9p6J8oa2jORZ6wxGX+2bp1a9AmlASmo39MQ/+Yhv6JsobmXOSJKAc7CQsXXnhh0CaUBKajf0xD/5iG/omyhuZc5InOzs6gTYg8mzdvDtqEksB09I9p6B/T0D9R1tCcizzR0NAQtAmRZ9GiRUGbUBKYjv4xDf1jGvonyhqac5EnbM6Ff7Zs2RK0CSWB6egf09A/pqF/oqyhORd5IsrZ68LCo48+GrQJJYHp6B/T0D+moX+irKFYTgz/iMh6YJlp6Q8RsRwtecB09I9p6B/T0D9h17C1tZUNGzZsUNXW5LqS6LkQkUtF5Mci0i4ivSLy3yJyu4jklPBDRHSU18ZC2W8YhmEYpURF0Ab4RUSuA34A9AHfA9qBdwL/CFwGvDvHJvcAa1KU7zt1Kw3DMAzj9CHSzoWINALfAGJAq6o+55XfATwF3CAi71XV7+bQ7G5VXZ13Yw3DMAzjNCHqwyI3ABOB7yYcCwBV7QP+t/f2j4MwzDAMwzBOVyLdcwFc6W1/mqLuaaAHuFREqlW1P8s2m0Tkj4ApwHFgs6rafAvDMAzDyJKoOxfzvO0ryRWqOiQiu4AFwBxgW5Ztng98c3iBiLwA3KKqL6Y5Zi64mbOGP0zD/GA6+sc09I9p6J8wa+jlPpmbqi7SS1FF5BXgbOBsVd2Rov4XwKXApar6bBbt/T1ucugruAmiLcCncMMvR4CFqro/xXHPA28BqkZpfg+wO5MNpzmzMI3ywSxMR7/MwjT0yyxMQ7/MItwazgXeUNULkisC77kQkd3AzBwOeUBVby6ELar6iaSi54B3i8hDwLuAPwc+nuK4k4Q1DMMwjNOVwJ0L4FVcL0G2HBj293FvOzbNvonyY7kalcTXcM7F23y2YxiGYRglT+DOhaou93H4y8BFwDnAiPRxIlIBzAaGgJ0+zgHwhret89mOYRiGYZQ8UV+K+pS3vTpF3duAMcAvc1gpko4l3tavk2IYhmEYJU/UnYuHcBMt3ysiFyUKRaQG+KL39v8OP0BExohIi4jMSCp/q4hUJp9ARN4K/LX39v6kuuki8m8ickBE+kVkt4jcJSLj/F9a6SMizSLyQRF5WER2eKHbj4vIz0Xkf4pI1O/PQBCRm4eFrf9g0PZECRFZ7t2PB73v9AEReVxE3hG0bVFARK4RkbUiss/7Pu8Uke+LyCVB2xYmROQGEfknEXlGRDq87+r9GY7JS5qLYhHp1SIAInI9zsnoA76LC/99LW6Z6kPAe3TYRYpIK/AzYESyFRFZgwsb/gzwGtCPWy1yNVCOiwR6a6ItETkL+CUwCXgU2A5cDFyBG665TFXbCnPVpYGI/C+c8/c67jPZC0wG/gA3X+YHwLs16jdpERGRM4EXcfdsPfAhVf3XYK2KBiLyFeAvcKH+f4L74TIRWAQ8qaqfDNC80CMifwt8EmgDHsHpNxf3/7gCeL+qjvoAPV0Qka24sAdduPuthVEWK4yS5mIe8JCq5prmovCoauRfuBwiPwaOAr24f64fB8pT7NsKKLA+qfx64IfADqADGMA99P4TuDZFO4977Xw0qfwfvPKvBa1L2F+4IGjvBMqSyqfgHA0F3hW0nVF5AQI8iZsk/X88/T4YtF1ReAEf8vRaA1SlqK8M2sYwv7zvbAw4CExKqrvC03Zn0HaG5eVpcrb3nU08k+5Ps28jcBj3g/eiYeU1uB+4Crw36GtKfkW+5yIIvF6LHbj1x2epanxYXQPOKRHcl6w7ECMjjoh8Bjcc9VVV/WjQ9kQBEbkNl7CvFee43Yn1XGRERKpxvZW9uJg5AwGbFDlEZDGwEXhMVa9LUd+B6ylvKLpxIWdYb3rKngsvYvQ3gX9X1Q8k1V0JrAOeVtVlRTA3a2xM+9S4wtuuHe5YAKhqJ/AL3GTSJckHGlkz6G2HArUiIojIfODLwN2q+nTQ9kSMFbjhjx8CcW/ewKdE5DabK5A1v8X19l4sIhOGV4jI24AGXK+akTtZp7konkmZCXwpakRJG3bc47fAStwS2XVFsaiE8JYRv997m+oLZQzD0+s+3FDSZwI2J4r8jrftA54Hzh1eKSJPAzeo6hvJBxoOVW0XkU/hhoVfEpFHcHMvzsLNuXgCuDVAE6NMIdJcFBxzLk6NRHCu42nqE+VNRbClFPky7h/8j1X18aCNiQCfAy4ALlfV3qCNiSCTvO1fAC8BS4GtuDg5f4f7ofB93HCTkQZVvcuLuPxvuDksCXYAa1T1cCCGRZ9IPm9sWMQIFSLyMeATuNU3twRsTujxxro/A/y9ZpE/x0hJ4v/gEG7y9s9VtUtdosLfx83mX2ZDJKMjIp/ErdBbg+uxqMOttNkJPOCtxjFOE8y5ODWKFXb8tEJE/hS4G/fr8QpVbQ/YpFDjDYf8O6679I6AzYkyie/p86q6e3iFqvbgVoaBW2pupMCblPi3uAmdf6aqO1W1R1W34By0/cAnRGROkHZGlEg+b8y5ODVe9rbnpKk/29umm5NhJCEitwP/BPwa51gcDNikKFCPuwfnA33DAmcpbqUIwDe8srsCszL8JL7P6f45H/W2tUWwJar8nrf9WXKF56D9Cve8sSSPuZP2eZPnNBd5xeZcnBqJL9BKESlLsRT1MtwM3o1BGBc1vIlgX8aNc69Q1SMBmxQV+nFL1FJxIe4f+c9x/5xsyCQ963CxAt6S/H32SEzw3FVcsyJFYqXCxDT1iXJb5ps7TwE34QI6fiepLpHm4mn1n+Yir1jPxSmgqq8Ca4FZwEeSqj+PG2u8z2JcZEZE7sA5FpuB5eZYZI+q9qrqB1O9gMe83b7tlX0vSFvDjKruwQXLmwHcNrxORFYCb8f1atjKpfQ8420/LCLThleIyO/ifnD14YI+GbmRc5qLMGBBtE6RFOG/twGLcTEwXgEuVQv/PSoi8gHc5K8Ybkgk1Wzo3aq6pohmlQQishoLopU1IjId930+E9eT8Tyuu/l63oyA+IPgLAw3Xh6gx4GrgE7gYVy0zvm4IRMBblfVuwMzMkR4aSuu995OwTmwO3nTSTuiqn+etH/WaS7CgDkXPvDyOHwB113VjIvM+TDweVU9OtqxxogH4GiMyAFjZIc5F7kjIhNxy3qvBc7ApQF4BviSqv4qSNuigJf48SPAe4G34Lrr23HzLe5R1bUBmhcqsvjft0dVZyUdcxnwWeASXOjvHbhlv/eoaqwwlp465lwYhmEYhpFXbM6FYRiGYRh5xZwLwzAMwzDyijkXhmEYhmHkFXMuDMMwDMPIK+ZcGIZhGIaRV8y5MAzDMAwjr5hzYRiGYRhGXjHnwjAMX4jIei9ZWskgImeLyMMictBL/FbwjJMisltEduehHRWR9f4tMoxTxxKXGUYIGPZw3gvMU9W+FPvsBmYClao6VETzTitEpBx4BJgL3Afsw4VdHu2YWbjEZt9W1VWFtdAwwo85F4YRLmYAt+OSuRnBMBsXvvobqvrhIp53eRHPZRgFxYZFDCM8HMXlYvhLEZkQtDGnMVO97YFinlRVX/UyLhtG5DHnwjDCQw/wV8BYMid0A0BEWr0x9tVp6k8axxeRVd4xq0RkhYg8IyJdIvKGiHxLRJq8/S4Qkf8SkaNe/WNe9386W6pF5IsisktE+kXkVRG5U0Sq0uzfIiJrROQ1ERkQkUMi8qCIzEux7xrP5jki8lER+W8R6c12boGILBKRH4jIYc+2PSLyLyJyRtJ+Cmzw3t7pnTOtvt4xq3FDIgAfGHaMisgqb58Tn5OIXCwiPxKRdq9slrdPqs9qrIj8hYg8JSL7PJ3e8D6LS7K5dq+dBhG5Q0R+LSIdItLpfT7fE5FF2bZjGNliwyKGES7+GfhT4FYRuUdVf1vAc12LS4f9X8DXgEuBVcAsEfk0LvX4M8A3gfOAdwJzROStqhpP0d5/AL+DSwE9CFwHrAYuEpFrh6eEFpGrgR8ClcB/4jI8Tgf+ALhGRK5Q1S0pznE3sBT4EfBjIGM2SBH5PeAHuLTfDwF7gEXAHwPXicjlqppwDj4PzAI+gHMy1nvl60nPeqAJuA14ATdfI8HWpH0vAT4N/ByX0XICMDBK2/OBvwaexl3zUdzQ2bXA74rIO1X1p6Mcj4gI8FPc5/ss8K/AEE7vK3Cf8ebR2jCMnFFVe9nLXgG/AAX2eX/f4L3/YdI+u73yimFlrV7Z6jTt7gZ2J5Wt8o4ZApYNKy8DnvDq2oGbko77pld3XVL5eq/8FWDcsPIa3MNMgVuGlY/DPSSPAG9JautcoAvYklS+xmtnPzA7B13rgTacE7I0qe5TXptrk8pH1TTNeWZ5x6xJU59oU4Fbc/isxgITUuw7HTdssy3NvbR+2PvzvLKHU+xbNvwzs5e98vWyYRHDCBmq+hDuofz7InJ5AU/1HVVNDAGgrjfiPu/tr1X1gaT9/93bLkzT3l+p6tFh7fXhfqUD/NGw/d6P+6V/p6q+NLwBVf018A3gAhF5S4pzfEXf7GXIhuuA8cD3VPWZpLq/xz3QV4jIjBza9MNWVb03251V9biqHklRvg/XC9OSg+29KdqJD//MDCNf2LCIYYSTTwC/BP4OWFKgczyXoiwxiTFVN/l+bzs9TXsbUpT9HNdrcMGwssRcgfPTzGU4x9vOB15KqvtVmnOn40Jv+1RyhaoOicjTuF6HC3DLgAtNrvYjIpfhhlwuASYByXNYpjG67S/hhmfeJyIzgUdxn8tzqjrakIxhnDLmXBhGCFHVZ0XkIeAGEfkfqvq9ApzmeIqyoSzqKtO0dyi5wHuAH8E9FBM0e9sPZbCvPkXZwQzHJDPW276epj5R3pRju6dKTvaLyO/jeij6cENWrwLdQBw31LIMqB6tDVWNiciVwOdwQ25/61V1isi3gU+ralcudhlGJsy5MIzw8mlct/6XROThNPskJlam+y43AQWPLukxmaRf0CJSgZu02DGsOOG4nK+q/53jOXKNBJo415Q09Wck7VdocrX/r3ATPi9S1W3DK0TkXpxzkfmkbujj48DHRWSud9ytuMnDTcAtOdplGKNicy4MI6So6g7gX3BBnT6aZrfEePmZyRXeQ2RscnkBSfWguxwoB54fVrbR2y4tuEVvnrc1ucJzfBI2pFqZkguJVSvlPttJZi7wUgrHogynbc6o6g5V/Sbu8+rCObCGkVfMuTCMcPMFXM/DZ0k9TLAd1ytwnYicGHoQkVrgnqJY+CZ3iMi4YTbUAF/y3n5r2H7fwl3TnSJycXIjIlImIq15sukR3MqX94lI8tyV23GO25Oq6ne+xVFcr0S+J4buBs4WkURgr8TS0tW4KKIZEZHZIjInRdU43JDKSRM9DcMvNixiGCFGVdtF5G+Ar6SpHxSRu4E7gOe94ZMKYAVucmYxo0xuA37jzRVJxLk4CxefIbEKBVVtE5EbgIeBjSKyDvgN7uF8Jm7iYjNuKasvVLVLRP4I+D6wQUS+jxu6WQSsxM2BuDVP59kELBWRB3DLcmPAY6cw9DOcf8TFIHleRH6A0/UynGPxn7jYI5k4H/ihiPw/3Gd0AJiI+3wqeXMOhmHkDXMuDCP83AP8CW5VQyruxEX3/BDwYdwD87u4X7fJqy0KyXtwTs5NuBDa+z0bvqyqI+YaqOo6EXkr8OfA23HDEwO4B99TuKBXeUFVH/VWXHzGO9dYnEZfwy2fzZcDdgvOGbgaeB8uaNc+4JSdC1W9V0T6cb0sH8D1MjwD/CHwLrJzLp7D5apZ5tk2DngDtyLoHlX9yanaZxjpkKTvvGEYhmEYhi9szoVhGIZhGHnFnAvDMAzDMPKKOReGYRiGYeQVcy4MwzAMw8gr5lwYhmEYhpFXzLkwDMMwDCOvmHNhGIZhGEZeMefCMAzDMIy8Ys6FYRiGYRh5xZwLwzAMwzDyyv8HYAE4e0KLyD8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# ---------- axis\n",
    "dx = 1    # margin in xtick\n",
    "ax.set_xlim([1-dx, ndata+dx])\n",
    "ax.set_ylim([-0.5, 3])\n",
    "\n",
    "# ---------- hline at zero\n",
    "ax.hlines(0.0, -dx, ndata+dx, 'k', '--')\n",
    "\n",
    "# ---------- plot\n",
    "#ax.plot(rslt_data['E_eV_atom'] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "\n",
    "\n",
    "# ---------- color coded by selection  \n",
    "tx = 0\n",
    "for s in range(1, smax+1):    # selection starts from 1\n",
    "    sfilter = rslt_data['Select'] == s\n",
    "    num = len(rslt_data[sfilter])\n",
    "    x = np.arange(1, num+1) + tx\n",
    "    ax.plot(x, rslt_data['E_eV_atom'][sfilter] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "    tx += num\n",
    "\n",
    "# ---------- title and label\n",
    "ax.set_title('Bayesian optimization for Na$_8$Cl$_8$')\n",
    "ax.set_xlabel('Number of trials')\n",
    "ax.set_ylabel('Energy (eV/atom)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- save figure\n",
    "fig.savefig('Na8Cl8_BO.png', bbox_inches='tight')    # PNG\n",
    "#fig.savefig('title_RS.png', bbox_inches='tight', dpi=300)    # high dpi PNG\n",
    "#fig.savefig('title_RS.pdf', bbox_inches='tight')    # PDF"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "toc": {
   "nav_menu": {
    "height": "84px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
